In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

The influence of data resampling on ensemble methods, and repeated cross-validation (RCV)-based ensemble feature selection (FS) is proposed. To evaluate the proposed method, support vector machine and its extension and recursive feature elimination were used as the underlying classification and FS techniques, respectively. Experimental evaluation was performed using four microarray datasets. The results show that especially for extremely small signature sizes, increasing ensemble size increases both classification performance and the robustness of gene selection (stability) for both RCV and bootstrap (BS). However, for ensembles of the same size, RCV outperforms BS in terms of performance and especially stability. When compared to the top results obtained by two other studies in which BS is utilised, RCV performs similar or better in terms of area under the receiver operator curve and better in terms of stability.

The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD.

Universal environmental contamination is a real situation that deteriorates our world step by step. The dairy factory out flowing is the second greatest source of contamination in water streams. The environmental impact of these factories can be very high, especially due to the discharge of wastewater with high content of organic matter and nutrients. These problems can be analyzed only after performing factual study of various physicochemical characteristics. In the presented study, physicochemical parameters like, temperature, pH, COD, TDS, TS and SS were taken into account. Another purpose of this study is to ascertain university student's awareness and consciousness against general and dairy products related environments. These levels were evaluated by the survey method. The data obtained from the questionnaire were analyzed using SPSS 20.0. Results showed that the public and company owners/ employees in particular should be informed about the seasonal and cheese variety dependent patterns in environmental pollution.

Ensemble feature selection (EFS) is a valuable technique for developing accurate and robust machine-learning (ML) models. Data variation plays a crucial role in the success of EFS models; however, it also causes some outliers in the ranked lists. In this study, we proposed the minimum weight threshold method-based EFS (MWT-EFS) to address the outlier problem and use the true power of EFS. The proposed method employs the support vector classifier to assign weights for features, and the MWT method handles outliers in the ranked feature lists while creating the ensemble list. First, a threshold value is determined. After that, the feature weights below the threshold are replaced with this value. This approach eliminates the negative effect of outliers. After the new feature weights are assigned, the average of the feature weights is calculated (mean aggregation) for all features, and the ensemble (final) feature list is created accordingly. The experiment results showed that the proposed method significantly improves gene selection stability while maintaining classification performance and reducing computational complexity. In conclusion, the proposed method led to an accurate and robust classification that can help domain experts to make 1616

Higher education institutions are an important variable in ensuring environmental sustainability in university campuses. The proposed researched on campus sustainability aims to contribute to the goal of yielding qualitative and result oriented university graduates in the context of Cyprus International University. This study examined the students' level of awareness in the science and technology building of the Cyprus International University in relation to the principles of a sustainable campus. In this study, a 24-question questionnaire was developed. In this study, it has been examined whether some students' personal characteristics (gender, program and class) continue to make a difference in their views on sustainability components such as curriculum and research, campus operations and community participation. The survey was designed according to quantitative relational screening model. For the validity of the questionnaire, expert opinion was obtained and reliability studies were performed and data were analyzed by using SPSS 20.0 program.Drawing from the results, it should be noted that even while students are concerned about the campus sustainability and corresponding environmental benefits, there seems to be less awareness in the area of transports, energy and water conservations on the campus. Furtermore, students mentioned that their cirruculum and student projects curricula have very little attention to sustainability. However, in order to promote the sustainability role on campuses, students are seen as key stakeholders in achieving this perception and goal. To achieve this goal students should be encouraged to incorporate the role of sustainability and other socially related issues. This study is important as it is a starting point for producing comprehensive goals and strategies for the campus sustainability of the

Objective: In our study the factors related to anesthesia and peroperative variables associated with postoperative mortality among patients aged ≥65 years who had undergone orthopedic surgery were assessed. Methods: Reports on patients aged ≥65 years who had undergone orthopedic surgery between 2015 and 2017 were investigated retrospectively. Results: A total of 135 patients were included in the study. The operations comprised implantations of total hip prosthesis in 26%, total knee prosthesis in 18%, fixation of lower extremity fractures in 24, and upper extremity fractures in 14%, and amputation surgery in 17% of the patients. The postoperative mortality rates were highest (76.9%) among patients who underwent amputation surgery (p<0.05). It was found that anesthesia type, whether regional or general, was not related to mortality. Mortality was found to be associated with increasing age, ≥3 ASA score, emergency surgery, ≥3 accompanying diseases, prolonged preoperative hospital stay and low preoperative hemoglobin (Hb) values (p<0.05). Patients developing postoperative complications, those who were monitored in intensive care unit (ICU) and required mechanical ventilator (MV), and patients with prolonged ICU and hospital stay had higher mortality rates (p<0.05). 9% of all patients were determined dead. Conclusion: Among geriatric orthopedic surgery patients, apart from gender and anesthesia method, increasing age, high ASA scores, emergency surgery, the number of accompanying diseases, duration of preoperative hospital stays, low preoperative Hb values, postoperative complications requiring ICU-MV and prolonged ICU and hospital stays were all factors that affected postoperative mortality. We believe that detailed preoperative assessment and perioperative clinical management are essential if postoperative prognosis after geriatric orthopedic surgery is to be improved.

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